Abstract: Disclosed herein is a method for non-destructive depth-profiling including projecting a pulsed pump beam into a specimen, projecting a pulsed probe beam thereinto, and sensing light returned therefrom to obtain a measured signal. Each probe pulse is configured to undergo Brillouin scattering off a primary acoustic pulse induced by the directly preceding pump pulse, so as to be scattered there off at a respective depth within the specimen. The method further includes executing an optimization algorithm configured to receive as inputs the measured signal, and/or a processed signal obtained therefrom, and output values of structural parameter(s) characterizing the specimen through minimization of a cost function indicative of a difference between the measured signal and a simulated signal obtained using a forward model simulating the scattering of a pulsed probe beam off at least the primary acoustic pulses.

Abstract: A method of evaluating a region of interest of a sample including: positioning the sample within in a vacuum chamber of an evaluation tool that includes a scanning electron microscope (SEM) column and a focused ion beam (FIB) column; acquiring a plurality of two-dimensional images of the region of interest by alternating a sequence of delayering the region of interest with a charged particle beam from the FIB column and imaging a surface of the region of interest with the SEM column; generating an initial three-dimensional data cube representing the region of interest by stacking the plurality of two-dimensional images on top of each other in an order in which they were acquired; identifying distortions within the initial three-dimensional data cube; and creating an updated three-dimensional data cube that includes corrections for the identified distortions.

Abstract: There is provided a system of examination of a semiconductor specimen, comprising a processor and memory circuitry configured to obtain, for each given candidate defect of a plurality of candidate defects in an image of the specimen, a given area of the given candidate defect in the image, obtain a reference image, perform a segmentation of at least part of the reference image, to determine, for each given candidate defect, first reference areas in the reference image matching a given reference area corresponding to the given area, select among the first reference areas, a plurality of second reference areas, obtain a plurality of corresponding second areas in the image, and use data informative of a pixel intensity of the second areas and data informative of a pixel intensity of the given area to determine whether the given candidate defect corresponds to a defect.

Abstract: There is provided a system and method of runtime examination of a semiconductor specimen. The method includes obtaining a runtime image representative of an inspection area of the specimen, the runtime image having a relatively low signal-to-noise ratio (SNR); and processing the runtime image using a machine learning (ML) model to obtain examination data specific for a given examination application, wherein the ML model is previously trained for the given examination application using one or more training samples, each training sample representative of a respective reference area sharing the same design pattern as the inspection area and comprising: a first training image of the respective reference area having a relatively low SNR; and label data indicative of ground truth in the respective reference area pertaining to the given examination application, the label data obtained by annotating a second training image of the respective reference area having a relatively high SNR.

Abstract: Disclosed are method and system for calibrating a tilt angle of an electron beam of a backscattered scanning electron microscope including scanning a bare wafer at a plurality of electron beam tilt and azimuth angles, thereby obtaining a calibration map representing a crystal orientation of the bare wafer, selecting a tilt angle and defining an expected diffraction pattern associated with the tilt angle, based on the calibration map; scanning a patterned wafer at the selected tilt angle, comparing the diffraction pattern of the image obtained from the scanning of the patterned wafer at the selected tilt angle with the expected diffraction pattern; correcting the tilt angle of the electron beam of the BSEM tool, such that the diffraction pattern of the image obtained during scanning of the patterned wafer will align with the expected diffraction pattern.

Abstract: A method for improving a quality of a secondary electron image of a region of a sample, the method may include obtaining a backscattered electron (BSE) image of the region and a secondary electron (SE) image of the region; wherein the BSE image and the SE image are generated by scanning of the region with an electron beam; processing the BSE image and the SE image to provide a processed BSE image and a processed SE image; and generating a BSE compensated SE image, wherein the generating comprises applying one or more selected BSE correction factors on one or more parts of the processed BSE image.

Abstract: A support unit for supporting a supported element, including (a) a spherical joint, (b) a pressure applying unit that is configured to maintain contact between a spherical outer surface and a base, (c) a position control unit that is configured to contact the spherical joint positioning element at multiple contact points and to set values of a first angle of rotation and a second angle of rotation of the spherical outer surface. The spherical joint is located above the position control unit. A distance between a fixed center of rotation and a point on the spherical outer surface is smaller than (b) a distance between the fixed center of rotation and any contact point of the multiple contact points.

Abstract: There is provided a method and a system configured to obtain metrology data Dmetrology informative of a plurality of structural parameters of a semiconductor specimen, obtain a model informative of a relationship between at least some of said structural parameters and one or more electrical properties of the specimen, use the model and Dmetrology to determine, for at least one given electrical property of the specimen, one or more given structural parameters among the plurality of structural parameters, which affect the given electrical property according to an impact criterion, and generate a recipe for an examination tool, wherein the recipe enables a ratio between a first acquisition rate of data informative of the one or more given structural parameters, and a second acquisition rate of data informative of other structural parameters of the plurality of structural parameters, to meet a criterion.

Abstract: A substrate alignment system that includes (i) an illumination unit that is configured to illuminate an illuminated region that comprises an entire edge of a substrate; (ii) a sensing unit having a field of view that covers the entire edge of the substrate even when the substrate is misaligned, the sensing unit includes a sensor that is preceded by a fish eye lens, the sensor is configured to generate detection signals of the entire edge of the substrate; and (iii) a processing circuit that is configured to process the detection signals and determine whether the substrate is misaligned. A determining that the substrate is misaligned triggers an execution of one or more misalignment correction operation for aligning the substrate.

Abstract: An apparatus that includes an end effector for handling and transporting wafers, the end effector including: a base portion having a first end adapted to be attached to a robot; a wafer support platform having a surface to support a wafer, a slidable joint coupling the base portion to the wafer support platform; and a sensor configured to detect when the wafer support platform slides relative to the base portion beyond a predetermined distance.

Abstract: A method for determining a depth of a hidden structural element of an object, the method may include (i) obtaining contrast information regarding a contrast between (a) hidden structural element detection signals that are indicative of electrons emitted from the hidden structural element, and (b) surroundings detection signals that are indicative of electrons emitted from a surroundings of the hidden structural element; wherein the hidden structural element detection signals and the surroundings detection signals are detected as a result of a scanning of a region of the object, with an illuminating electron beam; wherein the region comprises the hidden structural element and the surroundings; and (ii) determining the depth of the hidden structural element based, at least in part, on the contrast information.

Abstract: A method of depositing material over a sample in a deposition region of the sample with a charged particle beam column, the method comprising: positioning a sample within a vacuum chamber such that the deposition region is under a field of view of the charged particle beam column; cooling the deposition region by contacting the sample with a cyro-nanomanipulator tool in an area adjacent to the deposition region; injecting a deposition precursor gas into the vacuum chamber at a location adjacent to the deposition region; generating a charged particle beam with a charged particle beam column and focusing the charged particle beam on the sample; and scanning the focused electron beam across the localized region of the sample to activate molecules of the deposition gas that have adhered to the sample surface in the deposition region and deposit material on the sample within the deposition region.

Abstract: A method of determining a depth of a feature formed in a first region of a sample, by: positioning a test structure with known dimensions in a processing chamber having a charged particle column tilted at a first tilt angle and first rotational angle; determining the first tilt angle and first rotational angle by: taking an image of the test structure with the charged particle column tilted at the first tilt angle and the first rotational angle, measuring, based on the image, distances between multiple edges of the test structure aligned with each other along a vector, determining ratios between the measured distances, and determining a calculated tilt angle and a calculated rotational angle of charged particle column from the ratios and the known dimensions of the structure; transferring the test structure out of the processing chamber and positioning the sample in the processing chamber such that the first region is under a field of view of the charged particle column; taking a first image of the feature wi

Abstract: There is provided a system and method of defect detection of a semiconductor specimen. The method includes obtaining a first image of the specimen acquired at a first bit depth, converting by a first processor the first image to a second image with a second bit depth lower than the first bit depth, transmitting the second image to a second processor configured to perform first defect detection on the second image using a first defect detection algorithm to obtain a first set of defect candidates, and sending locations of the first set of defect candidates to the first processor, extracting, from the first image, a set of image patches corresponding to the first set of defect candidates based on the locations, and performing second defect detection on the set of image patches using a second defect detection algorithm to obtain a second set of defect candidates.

Abstract: Disclosed are an apparatus and method for generating a plurality of substantially collimated charged particle beamlets. The apparatus includes a charged particle source for generating a diverging charged particle beam, a beam splitter for splitting the charged particle beam in an array of charged particle beamlets, a deflector array includes an array of deflectors including one deflector for each charged particle beamlet of said array of charged particle beamlets, wherein the deflector array is configured for substantially collimating the array of diverging charged particle beamlets. The apparatus further includes a beam manipulation device configured for generating electric and/or magnetic fields at least in an area between the charged particle source and the deflector array. The apparatus has a central axis, and the beam manipulation device is configured for generating electric and/or magnetic fields substantially parallel to the central axis and substantially perpendicular to the central axis.

Abstract: A system for evaluating manufactured items that includes a memory module; an evaluation unit configured to execute instructions related to the evaluating of the manufactured items while applying a group of features; and a memory leakage unit configured to: select a first feature out of the group of features and disable an execution, by the evaluation unit, of instructions associated with the first feature at a presence of a memory leakage event. The first feature has a priority that is lower than a priority of a second feature of the group of feature. Priorities of features of the group of features are determined based on (i) priority information provided by one or more developers of the instructions related to the evaluating of the manufactured item, and (ii) usage information indicative of usage of the features of the group of features by the evaluation unit.

Abstract: A computerized system and method of training a deep neural network (DNN) is provided. The DNN is trained in a first training cycle using a first training set including first training samples. Each first training sample includes at least one first training image synthetically generated based on design data. Upon receiving a user feedback with respect to the DNN trained using the first training set, a second training cycle is adjusted based on the user feedback by obtaining a second training set including augmented training samples. The DNN is re-trained using the second training set. The augmented training samples are obtained by augmenting at least part of the first training samples using defect-related synthetic data. The trained DNN is usable for examination of a semiconductor specimen.

Abstract: A method for gray level ratio inspection comprising: obtaining an electron image that comprises region of interest (ROI) pixels of a ROI of the sample and reference pixels of a reference region of the sample, where the ROI pixels are obtained by illuminating the ROI with the electron beam and the reference pixels are obtained without illuminating the reference region with an electron beam; calculating a reference dark level value based on values of at least some of the reference pixels; calculating, responsive to the reference dark level value, a gray level ratio between a first gray level value related to a first sub-set of the ROI pixels and a second gray level value related to a second sub-set of the ROI pixels; determining whether the gray level ratio is indicative of a defect; and generating defect information following a determination that the gray level ratio is indicative of the defect.

Abstract: A system for discharging a region of a sample, the system includes (i) illumination optics that is configured to discharge the region by illuminating the region of the sample with a laser pulse during an illumination iteration; and (ii) a timing circuit that is configured to trigger the illumination iteration to occur at a timing that is based on one or more timing constraints associated with a scanning of the region by an electron beam.

Abstract: Disclosed herein is a system for non-destructive tomography of specimens. The system includes a scanning electron microscope (SEM) and a processor(s). The SEM is configured to obtain a sinogram of a tested specimen, parameterized by a vector {right arrow over (s)}, by projecting e-beams on the tested specimen, at each of a plurality of projection directions and offsets, and. for each e-beam, measuring a respective intensity of electrons returned from the tested specimen, The processor(s) is configured to obtain a tomographic map, pertaining to the tested specimen, by determining values indicative of components of a vector {right arrow over (t)} defined by an equation W{right arrow over (t)}={right arrow over (s)}. W is a matrix with components wij specifying a contribution of a j-th voxel in a nominal specimen to an i-th element of a nominal sinogram of the nominal specimen. The matrix W accounts for e-beam expansion and attenuation with depth within the nominal specimen.